L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimation in GLMs and in the Cox Model

Fitting possibly high dimensional penalized regression models. The penalty structure can be any combination of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a positivity constraint on the regression coefficients. The supported regression models are linear, logistic and Poisson regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters.


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install.packages("penalized")

0.9-51 by ORPHANED, 5 months ago


Browse source code at https://github.com/cran/penalized


Authors: Jelle Goeman , Rosa Meijer , Nimisha Chaturvedi , Matthew Lueder


Documentation:   PDF Manual  


Task views: Machine Learning & Statistical Learning, Survival Analysis


GPL (>= 2) license


Imports Rcpp

Depends on survival, methods

Suggests globaltest

Linking to Rcpp, RcppArmadillo


Imported by DIFboost, DIFlasso, apricom, gpDDE, hdnom, mispr, mvdalab, pensim.

Depended on by DIFtree, ROC632, lmmlasso, multiPIM, structree, subtype, uplift.

Suggested by MWLasso, catdata, confSAM, fscaret, lda, mlr, ordinalNet, peperr, riskRegression.


See at CRAN